Utilizing deep autoencoders can automatically discover the high-level compact representation of high-dimensional data. A notable example is CodeSLAM [1]} that encodes observed images into a compact and optimizable representation to contain the essential information of a dense scene. This general representation is further used into a keyframe-based SLAM system to infer both pose estimates and keyframe depth maps. Due to the reduced size of learned representations, CodeSLAM allows efficient optimization of tracking camera motion and scene geometry for a global consistency.
